Interference mitigation in ultra-dense wireless networks

ABSTRACT

Aspects for interference mitigation in ultra-dense networks are described.

PRIORITY CLAIM

This application claims the benefit of priority to U.S. ProvisionalPatent Application Ser. No. 62/572,327, filed Oct. 13, 2017, and titled“PARALLEL DYNAMIC CELL SELECTION AND LINK SCHEDULING FOR INTERFERENCEMANAGEMENT IN WIRELESS ULTRA-DENSE NETWORKS”, and to U.S. ProvisionalPatent Application Ser. No. 62/572,330, filed Oct. 13, 2017, and titled“CHANNEL FEEDBACK FOR INTERFERENCE MANAGEMENT IN WIRELESS ULTRA-DENSENETWORKS”, both of which are incorporated herein by reference in theirentirety.

TECHNICAL FIELD

Aspects pertain to wireless communications. Some aspects relate towireless networks including 3GPP (Third Generation Partnership Project)networks, 3GPP LTE (Long Term Evolution) networks, 3GPP LTE-A (LTEAdvanced) networks, and fifth-generation (5G) networks including newradio (NR) networks. Other aspects are directed to techniques, methodsand apparatuses for interference mitigation in ultra-dense wirelessnetworks and in networks in which vehicle-to-everything (V2X)communications are occurring.

BACKGROUND

In ultra-dense wireless networks, interference is caused by manywireless transmitters and receivers attempting to use the same wirelessresources simultaneously. Current systems for interference mitigationare inflexible and increase in complexity as the size of the networkgrows, making them unsuitable for ultra-dense wireless networks.

BRIEF DESCRIPTION OF THE DRAWINGS

In the figures, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The figures illustrate generally, by way of example, but notby way of limitation, various aspects discussed in the present document.

FIG. 1 illustrates an exemplary user device according to some aspects.

FIG. 2 illustrates an exemplary base station radio head according tosome aspects.

FIG. 3 illustrates exemplary communication circuitry according to someaspects.

FIG. 4 illustrates an exemplary multi-protocol baseband processoruseable in FIG. 1 or FIG. 2, according to some aspects.

FIG. 5 illustrates a wireless network in which example aspects can beimplemented.

FIG. 6A illustrates a first phase of signaling and feedback for channelmeasurement according to some aspects.

FIG. 6B illustrates a second phase of signaling and feedback for channelmeasurement according to some aspects.

FIG. 7 illustrates an algorithm for dynamic cell selection according tosome aspects.

FIG. 8 illustrates a method for transmission power control performed bythe central scheduler after scheduling according to some aspects.

FIG. 9 illustrates rate and proportional-fairness ratio estimationaccording to some aspects.

FIG. 10 illustrates non-orthogonal multiple access power allocationusing CQI feedback reports according to some aspects.

FIG. 11 demonstrates how checking the ITLinQ scheduling conditions canbe done using CQI feedback reports according to some aspects.

FIG. 12A illustrates a proportional fairness (PF) matrix at thebeginning of a user scheduling process according to some aspects.

FIG. 12B illustrates a PF matrix after scheduling of one user accordingto some aspects.

FIG. 12C illustrates a PF matrix after scheduling of two users accordingto some aspects.

FIG. 12D illustrates a PF matrix after scheduling of three usersaccording to some aspects.

FIG. 13 illustrates a method for sub-band dynamic cell selection andlink scheduling according to some aspects.

FIG. 14 illustrates a training mechanism for a network with a number ofpolicy gradient agents according to some aspects.

FIG. 15 illustrates machine learning-based multi-access edge computingaccording to some aspects.

FIG. 16 illustrates a machine learning solution for channel allocationin a vehicle according to some aspects.

FIG. 17 illustrates a block diagram of an example machine upon which anyone or more of the techniques (e.g., methodologies) discussed herein mayperform.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary user device according to some aspects.The user device 100 may be a mobile device in some aspects and includesan application processor 105, baseband processor 110 (also referred toas a baseband sub-system), radio front end module (RFEM) 115, memory120, connectivity sub-system 125, near field communication (NFC)controller 130, audio driver 135, camera driver 140, touch screen 145,display driver 150, sensors 155, removable memory 160, power managementintegrated circuit (PMIC) 165, and smart battery 170.

In some aspects, application processor 105 may include, for example, oneor more central processing unit (CPU) cores and one or more of cachememory, low drop-out voltage regulators (LDOs), interrupt controllers,serial interfaces such as SPI, I2C or universal programmable serialinterface sub-system, real time clock (RTC), timer-counters includinginterval and watchdog timers, general purpose IO, memory cardcontrollers such as SD/MMC or similar, USB interfaces, MIPI interfaces,and/or Joint Test Access Group (JTAG) test access ports.

In some aspects, baseband processor 110 may be implemented, for example,as a solder-down substrate including one or more integrated circuits, asingle packaged integrated circuit soldered to a main circuit board,and/or a multi-chip module including two or more integrated circuits.The memory 120 can store

FIG. 2 illustrates an exemplary base station or infrastructure equipmentradio head according to some aspects. A base station may be termed, forexample, a transmit point (TP), an Evolved Node-B (eNB, eNodeB), or aNew Radio Node-B (gNB, gNodeB). The base station radio head 200, in someaspects, may be configured to implement or execute a centralized powercontrol algorithm in order to mitigate interference as discussed in moredetail below. In some aspects, the base station radio head 200 mayinclude one or more of application processor 205, baseband processors210, one or more radio front end modules 215, memory 220, powermanagement integrated circuitry (PMIC) 225, power tee circuitry 230,network controller 235, network interface connector 240, satellitenavigation receiver (e.g., GPS receiver) 245, and user interface 250.

In some aspects, application processor 205 may include one or more CPUcores and one or more of cache memory, low drop-out voltage regulators(LDOs), interrupt controllers, serial interfaces such as SPI, I2C oruniversal programmable serial interface, real time clock (RTC),timer-counters including interval and watchdog timers, general purposeIO, memory card controllers such as SD/MMC or similar, USB interfaces,MIPI interfaces and Joint Test Access Group (JTAG) test access ports.

In some aspects, baseband processor 210 may be implemented, for example,as a solder-down substrate including one or more integrated circuits, asingle packaged integrated circuit soldered to a main circuit board or amulti-chip sub-system including two or more integrated circuits.

In some aspects, memory 220 may include one or more of volatile memoryincluding dynamic random access memory (DRAM) and/or synchronous DRAM(SDRAM), and nonvolatile memory (NVM) including high-speed electricallyerasable memory (commonly referred to as Flash memory), phase-changerandom access memory (PRAM), magneto-resistive random access memory(MRAM), and/or a three-dimensional cross point memory. Memory 220 may beimplemented as one or more of solder down packaged integrated circuits,socketed memory modules and plug-in memory cards.

In some aspects, power management integrated circuitry 225 may includeone or more of voltage regulators, surge protectors, power alarmdetection circuitry and one or more backup power sources such as abattery or capacitor. Power alarm detection circuitry may detect one ormore of brown out (under-voltage) and surge (over-voltage) conditions.

In some aspects, power tee circuitry 230 may provide for electricalpower drawn from a network cable. Power tee circuitry 230 may provideboth power supply and data connectivity to the base station radio head200 using a single cable.

In some aspects, network controller 235 may provide connectivity to anetwork using a standard network interface protocol such as Ethernet.Network connectivity may be provided using a physical connection whichis one of electrical (commonly referred to as copper interconnect),optical or wireless.

In some aspects, satellite navigation receiver 245 may include circuitryto receive and decode signals transmitted by one or more navigationsatellite constellations such as the global positioning system (GPS),Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS), Galileoand/or BeiDou. The receiver 245 may provide, to application processor205, data which may include one or more of position data or time data.Time data may be used by application processor 205 to synchronizeoperations with other radio base stations or infrastructure equipment.

In some aspects, user interface 250 may include one or more of buttons.The buttons may include a reset button. User interface 250 may alsoinclude one or more indicators such as LEDs and a display screen.

FIG. 3 illustrates exemplary communication circuitry according to someaspects. Communication circuitry 300 shown in FIG. 3 may bealternatively grouped according to functions. Components illustrated inFIG. 3 are provided here for illustrative purposes and may include othercomponents not shown in FIG. 3.

Communication circuitry 300 may include protocol processing circuitry305 (or processor) or other means for processing. Protocol processingcircuitry 305 may implement one or more of medium access control (MAC),radio link control (RLC), packet data convergence protocol (PDCP), radioresource control (RRC) and non-access stratum (NAS) functions, amongothers. Protocol processing circuitry 305 may include one or moreprocessing cores to execute instructions and one or more memorystructures to store program and data information.

Communication circuitry 300 may further include digital basebandcircuitry 310. Digital baseband circuitry 310 may implement physicallayer (PHY) functions including one or more of hybrid automatic repeatrequest (HARQ) functions, scrambling and/or descrambling, coding and/ordecoding, layer mapping and/or de-mapping, modulation symbol mapping,received symbol and/or bit metric determination, multi-antenna portpre-coding and/or decoding which may include one or more of space-time,space-frequency or spatial coding, reference signal generation and/ordetection, preamble sequence generation and/or decoding, synchronizationsequence generation and/or detection, control channel signal blinddecoding, link adaptation, and other related functions.

Communication circuitry 300 may further include transmit circuitry 315,receive circuitry 320 and/or antenna array circuitry 330. Communicationcircuitry 300 may further include RF circuitry 325. In some aspects, RFcircuitry 325 may include one or multiple parallel RF chains fortransmission and/or reception. Each of the RF chains may be connected toone or more antennas of antenna array circuitry 330.

In some aspects, protocol processing circuitry 305 may include one ormore instances of control circuitry. The control circuitry may providecontrol functions for one or more of digital baseband circuitry 310,transmit circuitry 315, receive circuitry 320, and/or RF circuitry 325.

FIG. 4 illustrates a multi-protocol baseband processor 400 useable inthe system and circuitry shown in FIG. 1 or FIG. 2, according to someaspects. In an aspect, baseband processor may contain one or moredigital baseband subsystems referred to collectively as digital basebandsubsystems 440.

In an aspect, the one or more digital baseband subsystems 440 may becoupled via interconnect subsystem 465 to one or more of CPU subsystem470, audio subsystem 475 and interface subsystem 480. In an aspect, theone or more digital baseband subsystems 440 may be coupled viainterconnect subsystem 445 to one or more of each of digital basebandinterface 460 and mixed-signal baseband subsystem 435.

In an aspect, interconnect subsystem 465 and 445 may each include one ormore of each of buses point-to-point connections and network-on-chip(NOC) structures. In an aspect, audio subsystem 475 may include one ormore of digital signal processing circuitry, buffer memory, programmemory, speech processing accelerator circuitry, data convertercircuitry such as analog-to-digital and digital-to-analog convertercircuitry, and analog circuitry including one or more of amplifiers andfilters. In an aspect, interconnect subsystem 465 and 445 may eachinclude one or more of each of buses point-to-point connections andnetwork-on-chip (NOC) structures.

Power Control Methods for Interference Mitigation

FIG. 5 illustrates a wireless network 500 in which example aspects canbe implemented. Without loss of generality, the wireless network 500 isshown to have a plurality of UEs and base stations (also referred toherein as transmit points (TPs). Previous power control methods forinterference mitigation used binary power control to turn off TPs in thenetwork in order to reduce the interference among the remaining TPs. Theremaining TPs would transmit at full power to communicate downlink withscheduled UEs. This power control method is inflexible and becomeshighly complex in large networks. Moreover, the performance gains can belimited when remaining TPs transmit at full power rather than accuratelyfine tuning power levels.

Centralized Greedy Method for Transmit Power Control

Methods, systems and apparatuses according to aspects providecentralized greedy transmit power control. In aspects, transmit powerlevels for TPs in the wireless network 500 are optimized in thedescending order according to the priority of any UE/s associated withthat respective TP. The UE priority might be defined for example basedon proportional fairness (PF) ratio. Optimization may be performed, forexample, in processing circuitry 502 of a scheduler 504, wherein thescheduler 504 is responsible for scheduling TPs and UEs within an area505.

In aspects, the processing circuitry 502 can utilize a single-pass blockcoordinate ascent algorithm that optimizes the transmit power of thecorresponding TP such that an overall network objective function (e.g.,sum rate) is maximized. For example, if a TP is causing littleinterference, that TP may transmit with a higher power than another TPthat is causing large amounts of interference.

Referring to FIG. 5, the wireless network 500 includes N TPs{TP_(i)}_(i=1) ^(N) and

K  UEs{UE_(j)}_(j = 1)^(K),located arbitrarily within an area. In the example illustrated in FIG.5, N=5 TPs and K=22 UEs. However, it will be understood that methodsimplemented in accordance with some aspects can include more or fewerTPs and UEs than depicted in FIG. 5.

Methods according to some aspects are described with reference to asingle-input single-output (SISO) flat fading channel. The SISO flatfading channel is equivalent to a post-processing channel of anorthogonal frequency division multiple access (OFDMA) tone. Thisequivalency implies that methods according to aspects can be applied toa frequency selective multiple-input multiple-output (MIMO) OFDMAsystem. In Equation 1, below, the channel gain between TP_(i) and UE_(j)at scheduling interval t is g_(ji)h_(ji)(t), where g_(ji) and h_(ji)(t)respectively denote the long-term component of the channel gain(corresponding to path-loss and shadowing) and the short-term componentof the channel gain (corresponding to short-term fading). It is furtherassumed that all transmissions occur in the same frequency band andinterfere on each other. Therefore, at each scheduling interval t,assuming synchronous transmission of the TPs, the received signal ofUE_(j) is:Y _(j)(t)=Σ_(i=1) ^(N) g _(ji) h _(ji)(t)X _(i)(t)+Z _(j)(t)  (1)where X_(i)(t) denotes the transmit signal of TP_(i) at schedulinginterval t, subject to a maximum transmission power P, and Z_(j)(t)denotes the additive white Gaussian noise at UE_(j) in schedulinginterval t, distributed as a complex Gaussian random variable with mean0 and variance σ².

Processing circuitry 502 attempts to maximize a cost function (forexample, weighted sum-rate) for an overall network 500 using acentralized power control algorithm described below. The power controlalgorithm described below can be utilized in conjunction with otherinterference management techniques. The power control performedaccording to aspects can be viewed as continuous power control, which inturn can be viewed as a generalization of binary (e.g., ON/OFF or 0/1)power control in which TPs are merely turned on or off to mitigateinterference.

Initially, each UE gets associated with the TP to which, for example,each respective UE has the strongest long-term channel gain (lowestpath-loss+shadowing). This association can be updated periodically, upondetection of a change in the wireless network 500, or at any otherinterval. Each transmit point TP_(i) will maintain a list of the UEsassociated with each respective TP_(i).

The UE list of each TP is ordered according to a given prioritycriterion. One priority criterion is proportional fairness (PF). Supposethat R _(j)(t−1) denotes the long-term average rate of UE_(j) atscheduling interval t−1 (to be defined later below). Then, at the startof the scheduling interval t, the PF ratio for each UE_(j) is:

$\begin{matrix}{{PF}_{j} = \frac{{\overset{\sim}{r}}_{j}(t)}{{\overset{\_}{R}}_{j}\left( {t - 1} \right)}} & (2)\end{matrix}$where {tilde over (r)}_(j)(t) denotes the estimated instantaneous ratethat UE_(j) receives from TP_(i) _(j) in the scheduling interval t. ThePF ratio can be calculated by, for example, the baseband processor 210(FIG. 2) of a TP. {tilde over (r)}_(j)(t) be written as:

$\begin{matrix}{{{\overset{\sim}{r}}_{j}(t)} \approx {\log_{2}\left( {1 + \frac{P{{{\overset{\sim}{g}}_{{ji}_{j}}(t)}}^{2}}{{\sum\limits_{i \in {T{\{ i_{j}\}}}}{P{{\overset{\_}{\gamma}}_{i}(t)}{{{\overset{\sim}{g}}_{ji}(t)}}^{2}}} + {\sum\limits_{i \in S_{T}}{P{{{\overset{\sim}{g}}_{ji}(t)}}^{2}}} + \sigma^{2}}} \right)}} & (3)\end{matrix}$

After calculating the PF ratios for the UEs, according to (2) thebaseband processor 210 of each TP will perform UE scheduling byselecting the UE in its list with the highest priority (e.g. PF ratio)for communication. At each scheduling interval, after the UE schedulingphase is completed, the wireless network 500 includes N TPs that maycommunicate with N UEs. The set of TPs can be denoted by {TP_(i)}_(i=1)^(N) and the set of scheduled UEs can be denoted by {UE_(i)}_(i=1) ^(N),where for each i∈{1, . . . , N}, TP_(i) intends to communicate toUE_(i).

The processing circuitry 502 will then execute a power control algorithmby designating an order for the pairs. For example, the power controlalgorithm can sort the TP-UE pairs in descending order of PF ratio (orother fairness or priority criterion) for the corresponding UEs:PF₁≥PF₂≥ . . . ≥PF_(N)  (4)where for any i∈{1, . . . , N}, PF_(i) denotes the PF ratio of UE_(i).

The scheduler 504 will further include memory 506 to store at least theTP-UE pairs. The scheduler 504 will provide messages to each TPinstructing each TP to adjust transmission power to optimize theaggregate network weighted sum-rate (or any other relevant networkmetric or optimization function). In particular, the scheduler 504provides a message over a control channel that instructs each TP_(i) toadjust transmission power based on the following optimization problem:

$\begin{matrix}{P_{i}^{*} = {{\arg\;{\max\limits_{P_{i}}{\sum\limits_{j = 1}^{i - 1}{w_{j}{\log\left( {1 + \frac{P_{j}^{*}{\overset{\sim}{g}}_{jj}}{{P_{i}{\overset{\sim}{g}}_{ji}} + {\sum\limits_{\underset{k \neq j}{k = 1}}^{i - 1}{P_{k}^{*}{\overset{\sim}{g}}_{jk}}} + {\sum\limits_{k = {i + 1}}^{N}{P{\overset{\sim}{g}}_{jk}}} + \sigma^{2}}} \right)}}}}} + {w_{i}{\log\left( {1 + \frac{P_{i}{\overset{\sim}{g}}_{ii}}{{\sum\limits_{j = 1}^{i - 1}{P_{j}^{*}{\overset{\sim}{g}}_{ij}}} + {\sum\limits_{j = {i + 1}}^{N}{P{\overset{\sim}{g}}_{ij}}} + \sigma^{2}}} \right)}} + {\sum\limits_{j = {i + 1}}^{N}{w_{j}{\log\left( {1 + \frac{P{\overset{\sim}{g}}_{jj}}{{P_{i}{\overset{\sim}{g}}_{ji}} + {\sum\limits_{k = 1}^{i - 1}{P_{k}^{*}{\overset{\sim}{g}}_{jk}}} + {\sum\limits_{\underset{k \neq j}{k = {i + 1}}}^{N}{P{\overset{\sim}{g}}_{jk}}} + \sigma^{2}}} \right)}}}}} & (5)\end{matrix}$where for the TPs whose power levels have already been set({TP_(j)}_(j=1) ^(i-1)), the corresponding optimized power levels

({P_(j)^(*)}_(j = 1)^(i − 1))are used in the objective function (e.g., sum rate), while for the TPswhose power levels have not yet been set, e.g.,

{TP_(j)}_(j = i + 1)^(N),the respective TP transmits at full transmission power P. Moreover, foreach UE_(j), w_(j) denotes the weight of the user, which can be, forexample, the inverse of the long-term average rate of the UE. Thescheduler 504 may provide messages to TPs sequentially according topriority of a respective pair of the list of pairs.

{tilde over (g)}_(ji) represents the estimate of the actual channel gainbetween TP_(i) and UEj. It is assumed that the actual channel gain isbased on measurements periodically reported back from the UEs to theTPs. It is further understood that the scheduler 504 includestransceiver circuitry 508 for communicating over the control channel.

For any TP_(i), i∈{1, . . . , N}, if P*_(i)≤0.1 P, then set P*_(i)=0.Therefore, the minimum transmission power level for each TP is 10% ofthe full transmission power, otherwise the corresponding TP gets turnedoff (e.g., power for the corresponding TP is zero).

In some aspects, the power control algorithm described above withrespect to Equations (1)-(5) can be executed by the processing circuitry502 through several iterations on each TP until convergence.

Recently, there has been a surge of interest in non-orthogonal multipleaccess (NOMA) as a method for improving the performance of wirelessnetworks. As compared to orthogonal multiple access (OMA), NOMA allowsthe superposition of signals of multiple receivers at a singletransmitter, which can potentially lead to higher user data rates. Userscheduling according to some aspects can be performed in wirelessnetworks 500 that use NOMA.

In aspects, use of NOMA will affect the objective function as describedbelow. For example, in each scheduling interval, each TP can select theUE in its UE list having the highest priority (e.g. PF ratio) forcommunication. Then, as the UE scheduling process continues according toaspects, each TP will search for a second UE in its UE list that,together with the first UE, maximizes the weighted sum-rate of the UEsif they are served by the TP using NOMA. In particular, without loss ofgenerality, assuming TP₁ has selected UE₁ as its highest priority UE andis searching for a second UE in the remainder of its association listL₁\{1}. For any UE_(j), j∈L₁\{1}, the weighted sum-rate can be writtenasf _(j)(β_(j))=w ₁ r _(1,NOMA)(β_(j))+w _(j) r _(j,NOMA)(β_(j))  (6)where β_(j) denotes the NOMA power allocation variable for UE_(j),r_(1,NOMA)(β_(j)) and r_(j,NOMA)(β_(j)) respectively denote the rates ofUE₁ and UE_(j) under NOMA and w₁ and w_(j) respectively denote theweights of UE₁ and UE_(j). In accordance with the proportional fairnesscriterion, at each scheduling interval, the UE weights are assumed to bethe inverse of their long-term average rates. In particular, for anyUE_(j):

$\begin{matrix}{w_{j} = \frac{1}{{\overset{\_}{R}}_{j}\left( {t - 1} \right)}} & (7)\end{matrix}$

To maximize the weighted NOMA sum-rate of each pair of UEs, apparatusesand methods according to aspects will optimize the corresponding NOMApower allocation variable. In performing this optimization, assume thatin the current scheduling interval UE₁ has a better channel quality toTP₁ than UE_(j), which can be expressed as {tilde over (g)}₁₁(t)>{tildeover (g)}_(j1)(t). To serve both UEs, the baseband processor 210 of TP₁will do superposition coding, allocating a fraction 0≤β_(j)≤1 of itspower to UE_(j) (e.g., the “weak” UE) and the remaining 1−β_(j) fractionof its power to UE₁ (e.g., the “strong” UE). In this way, the strong UEuses successive interference cancellation (SIC) to decode its message.In aspects, the strong UE first decodes the message of the weak UE,temporarily treating its own message as noise, then subtracts itscontribution from its received signal to cancel its interference andfinally decodes its own signal. The weak UE decodes its message bytreating the interference due to the strong UE's message as noise.

Using the aforementioned encoding and decoding, the rates of each of theUEs can be written as

$\begin{matrix}{\mspace{79mu}{{r_{1,{NOMA}}\left( \beta_{j} \right)} = {\log_{2}\left( {1 + \frac{{P\left( {1 - \beta_{j}} \right)}{{{\overset{\sim}{g}}_{11}(t)}}^{2}}{{\sum\limits_{i = 2}^{N}{P\;{\gamma_{i}\left( {t - 1} \right)}{{{\overset{\sim}{g}}_{1i}(t)}}^{2}}} + \sigma^{2}}} \right)}}} & (8) \\{{r_{{j,{NOMA}}\;}\left( \beta_{j} \right)} = {\min\begin{Bmatrix}{{\log_{2}\left( {1 + \frac{P\;\beta_{j}{{{\overset{\sim}{g}}_{11}(t)}}^{2}}{{\sum\limits_{i = 2}^{N}{P\;{\gamma_{i}\left( {t - 1} \right)}{{{\overset{\sim}{g}}_{1i}(t)}}^{2}}} + {{P\left( {1 - \beta_{j}} \right)}{{{\overset{\sim}{g}}_{11}(t)}}^{2}} + \sigma^{2}}} \right)},} \\{\log_{2}\left( {1 + \frac{P\;\beta_{j}{{{\overset{\sim}{g}}_{j\; 1}(t)}}^{2}}{{\sum\limits_{i = 2}^{N}{P\;{\gamma_{i}\left( {t - 1} \right)}{{{\overset{\sim}{g}}_{ji}(t)}}^{2}}} + {{P\left( {1 - \beta_{j}} \right)}{{{\overset{\sim}{g}}_{j\; 1}(t)}}^{2}} + \sigma^{2}}} \right)}\end{Bmatrix}}} & (9)\end{matrix}$where in Equation (8), the first term in the minimum corresponds to therate of decoding the weak UE_(j)'s message at UE₁ and the second termcorresponds to the rate of decoding the weak UE_(j)'s message at UE_(j).Assuming that the latter rate is the smaller of the two,r_(j,NOMA)(β_(j)) can be simplified to:

$\begin{matrix}{{r_{j,{NOMA}}\left( \beta_{j} \right)} = {\log_{2}\left( {1 + \frac{P\;\beta_{j}{{{\overset{\sim}{g}}_{j_{1}}(t)}}^{2}}{{\sum\limits_{i = 2}^{N}{P\;{\gamma_{i}\left( {t - 1} \right)}{{{\overset{\sim}{g}}_{ji}(t)}}^{2}}} + {{P\left( {1 - \beta_{j}} \right)}{{{\overset{\sim}{g}}_{j_{1}}(t)}}^{2}} + \sigma^{2}}} \right)}} & (10)\end{matrix}$

The weighted sum-rate for the above two UEs can be written as:

$\begin{matrix}{{f_{j}\left( \beta_{j} \right)} = {{w_{1}{\log_{2}\left( {1 + \frac{{P\left( {1 - \beta_{j}} \right)}{{{\overset{\sim}{g}}_{11}(t)}}^{2}}{{\sum\limits_{i = 2}^{N}{P\;{\gamma_{i}\left( {t - 1} \right)}{{{\overset{\sim}{g}}_{1i}(t)}}^{2}}} + \sigma^{2}}} \right)}} + {w_{j}\;{\log_{2}\left( {1 + \frac{P\;\beta_{j}{{{\overset{\sim}{g}}_{j_{1}}(t)}}^{2}}{{\sum\limits_{i = 2}^{N}{P\;{\gamma_{i}\left( {t - 1} \right)}{{{\overset{\sim}{g}}_{ji}(t)}}^{2}}} + {{P\left( {1 - \beta_{j}} \right)}{{{\overset{\sim}{g}}_{j_{1}}(t)}}^{2}} + \sigma^{2}}} \right)}}}} & (11)\end{matrix}$

It can be shown that the above weighted sum-rate is maximized at:

$\begin{matrix}{\beta_{j}^{*} = {1 - \frac{\begin{matrix}{{w_{1}{{{\overset{\sim}{g}}_{11}(t)}}^{2}\left( {{\sum\limits_{i = 2}^{N}{P\;{\gamma_{i}\left( {t - 1} \right)}{{{\overset{\sim}{g}}_{ji}(t)}}^{2}}} + \sigma^{2}} \right)} -} \\{w_{j}{{{\overset{\sim}{g}}_{j_{1}\;}(t)}}^{2}\left( {{\sum\limits_{i = 2}^{N}{P\;{\gamma_{i}\left( {t - 1} \right)}{{{\overset{\sim}{g}}_{1i}(t)}}^{2}}} + \sigma^{2}} \right)}\end{matrix}}{{{{\overset{\sim}{g}}_{11}(t)}}^{2}{{{\overset{\sim}{g}}_{j_{1}\;}(t)}}^{2}{P\left( {w_{j} - w_{1}} \right)}}}} & (12)\end{matrix}$

It is determined whether β*_(j) is feasible by checking whether β*_(j)is within the interval [0.5,1]. If so, set f*_(j)=f_(j)(β*_(j)).Otherwise, set f*_(j)=0, indicating that UE_(j) cannot be served by NOMAtogether with UE₁.

After the above calculations, TP₁ will check whether it achieves ahigher weighted sum-rate if it operates in NOMA mode compared to thesingle-user mode, only serving UE₁; i.e., it checks whether

${\max\limits_{j \in {L_{1}\text{\textbackslash}{\{ 1\}}}}\; f_{j}^{*}} > {PF}_{1}$

If the above condition is satisfied, let UE_(j*) be the UE which yieldsthe highest weighted NOMA sum-rate together with UE₁:

$\begin{matrix}{j^{*} = {\arg\;{\max\limits_{j \in {L_{1}\text{\textbackslash}{\{ 1\}}}}\; f_{j}^{*}}}} & (13)\end{matrix}$

Then both UE₁ and UE_(j*) are scheduled to be served by TP₁ in NOMAmode. Otherwise, only UE₁ is scheduled to be served by TP₁ insingle-user mode.

When user scheduling involves NOMA, a similar power control algorithmmay be applied with some minor differences. TPs are prioritized basedon, for example, the weighted sum-rate of their scheduled UE(s), whetherthey are in single-user mode or in NOMA mode. Moreover, the rates ofboth strong and weak UEs shall be included in the objective function forthe TPs that are operating in NOMA mode.

Parallel Dynamic Cell Selection and Power Control

Methods and apparatuses according to some aspects provide dynamic cellselection in conjunction and in parallel with power control to reduce oreliminate interference in the network 500. A UE may be paired with abest-available TP, and the pairing can be dynamically changed asinterference conditions and other conditions in the network 500 change.

According to some aspects, each UE can adjust its associated TP if thestrongest-available TP is not available. For example, a strongest TP mayalready be serving other UEs and unable to serve one particular UE orany additional UEs. In some aspects, the UE having highest priority isassigned to a TP from which it is receiving the strongest signal, andthe TP-UE pair is scheduled, by a central scheduler 504, fortransmission at an adjusted power.

FIG. 6A illustrates signaling and feedback for channel measurement in anetwork having two TPs (or base stations (BSs)) and two UEs according tosome aspects. FIG. 6A illustrates signaling and feedback for channelmeasurement in a network having two TPs (or base stations (BSs)) and twoUEs according to some aspects. As illustrated, each of UE₁ and UE₂measure gain between each of TP₁ and TP₂. Methods according to aspectsrely on UEs frequently or periodically measuring and reporting thechannel state information between the neighboring TPs and the UEs. As anexample, for a given measurement period T, at each scheduling intervalt=nT, each UE_(j) reports

${{\overset{\sim}{g}}_{ji}({nT})} = \sqrt{\frac{1}{nT}g_{ji}^{2}{\sum\limits_{t = 1}^{nT}{{h_{ji}(t)}}^{2}}}$back to TP_(i), where {tilde over (g)}_(ji)(t) represents theapproximate measurement of the actual channel gain between TP_(i) andUE_(j). For t≠nT,

${{\overset{\sim}{g}}_{ji}(t)} = {{{\overset{\sim}{g}}_{ji}\left( {\left\lfloor \frac{t}{T} \right\rfloor T} \right)}.}$The UE can perform measurements via pilot signals that the TPs send overdistinct tones.

At each scheduling interval, the central scheduler 504 implements ascheduling algorithm 700 as illustrated in FIG. 7 having at most min{N,K} iterations, where N is the number of TPs and K is the number of UEsin the area 505. In each iteration, a single UE or a pair of UEs areconsidered as candidate(s) to be served by a potential TP. The centralscheduler 504 will schedule that potential TP to serve the candidateUE/s if the UE/s is/are not receiving significant interference from theTPs that were scheduled in previous iterations of the schedulingalgorithm 700 and if the potential TP is not significantly interferingwith UE/s that have already been scheduled. If the UE/s and TP arescheduled, the UE/s and TP are removed from the set of available UEs andTPs. The amount of interference needed here to prevent scheduling can beset by an operator to a predetermined threshold, or can be set accordingto a standard, although aspects are not limited thereto. The centralscheduler 504 continues with further iterations of the schedulingalgorithm 700 until there are no more UEs or TPs available to bescheduled.

The scheduling algorithm 700 begins with operation 702 with the centralscheduler 504 initializing the set of T (available TPs) and U (availableUEs to include all TPs and UEs:T={1,2, . . . ,N}  (14)U={1,2, . . . ,K}  (15)

Moreover, let S_(T) and S_(U), respectively, denote the set of TPs andUEs that have been already scheduled. These should be initially set tothe null set.

The central scheduler 504 then continues with operation 704 byassociating each available UE of the list of available UEs with theavailable TP based on a value representative of signal strength betweeneach UE and the respective TP. For example, the respective UE may beassociated to the TP with which the UE has the strongest long-termchannel gain. In particular, UE_(j), j∈U gets associated with TP_(i)_(j) where

$\begin{matrix}{i_{j} = {\arg\;{\max\limits_{i \in T}{g_{ji}}}}} & (16)\end{matrix}$

The central scheduler 504 orders the UEs according to a given prioritycriterion, for example PF as described earlier herein with respect toEquation (2).

After calculating the PF ratios for the available UEs, the centralscheduler 504 continues with operation 706 by selecting the available UEwith the highest PF ratio, denoted by UE_(j*) and its serving TP_(i)_(j*) . The TP may operate in single-user mode, only serving UE_(j*), oroperate in NOMA mode and serve another UE alongside UE_(j*). In the NOMAcase, the TP can perform UE scheduling as described earlier herein toselect a potential secondary UE_(j′*) in the TP's association list,which helps increase the weighted sum-rate compared to the single-userPF ratio.

Upon determination of the currently selected TP_(i) _(j*) and the singleUE or the pair of UEs that TP_(i) _(j*) intends to serve, the centralscheduler 504 implements a link scheduler portion in operation 708 todetermine whether the selected TP should be activated and begin servingthe selected UE(s). In some aspects, the central scheduler 504 willschedule according to a full-reuse method, in which the selected TP andUE(s) will always be scheduled. In other aspects, the central scheduler504 determines whether the selected TP is causing strong or weakinterference at UEs in S_(U) that are already scheduled, and alsowhether the selected UE/s is/are receiving strong/weak interference fromthe TPs in S_(T) that are already scheduled.

Upon checking the above-described conditions, there are three possiblecases. In the case of strong outgoing interference, the TP will not bescheduled to serve the selected UE(s) and the selected TP will beremoved from the available TP list according to T←T\{i_(j*)} inoperation 710. In the case of strong incoming interference, the TP willnot be scheduled to serve the selected UE(s) and the selected UE(s) willbe removed from the available UE list also in operation 710. Therefore,in single-user mode: U←U\{j*}, and in NOMA mode: U←U\{j*, j′*}.

In the case of weak incoming and outgoing interference, the TP will bescheduled to serve the selected UE(s) and both TP and UE/s will beremoved from the list in operation 712. In operation 714, the centralscheduler 504 therefore updates the set of scheduled and available TPsaccording to S_(T)←S_(T)∪{i_(j*)} and T←T\{i_(j*)}. In single-user mode,the set of scheduled and available UEs is updated: S_(U)←S_(U)∪{j*} andU←U\{j*}. In NOMA mode, the sets should be updated: S_(U)←S_(U)∪{j*,j′*} and U←U\{j*, j′*}.

After operation 714, the central scheduler 504 continues with furtheriterations at operation 706. Operations 706, 708, 710, 712, and 714 areiteratively implemented where appropriate until no available UEs and TPsremain.

The central scheduler 504 can implement transmission power control inconjunction with scheduling, either after scheduling has been performedor concurrently with scheduling.

FIG. 8 illustrates a method 800 for transmission power control performedby the central scheduler 504 after scheduling according to some aspects.The method 800 begins with operation 802 with the central scheduler 504initializing the set of T (available TPs) and U (available UEs toinclude all TPs and UEs, similarly to operation 702 and Equations (14)and (15) discussed earlier herein.

The central scheduler 504 then continues with operation 804 byassociating each available UE of the list of available UEs with theavailable TP to which, for example, the respective UE has the strongestlong-term channel gain, similarly to operation 704 and Equation (16)discussed above. The central scheduler 504 orders the UEs according to agiven priority criterion, for example PF as described earlier hereinwith respect to Equation (2).

After calculating the PF ratios for the available UEs, the centralscheduler 504 determines whether there are available UEs and TPs atoperation 806. If there are no more available UEs and TPs, the centralscheduler 504 implements power control methods at operation 808 asdescribed above with reference to at least Equation (5). Else, thecentral scheduler 504 continues with operation 810 by selecting theavailable UE with the highest PF ratio, denoted by UE_(j*) and itsserving TP_(i) _(j*) . At operation 812, the central scheduler 504schedules the current UE/TP pair with the TP to operate at full power.In operation 814, the scheduled UEs and TPs are removed from listssimilarly to operation 710 (FIG. 7) and priorities and associations areupdated similarly to operation 714 (FIG. 7).

In aspects in which power control is performed concurrently withscheduling, operation 808 will be omitted and those functionalities willbe performed with operation 812.

Feedback-Based Methods for Interference Management

Methods and apparatuses according to some aspects utilize periodicfeedback of the channel quality indicator (CQI) measured at the UEs,while minimizing the feedback overhead in terms of number of channelmetrics reported per UE. Such feedback reports can help enable theinterference management schemes described earlier herein. Overhead canbe minimized by taking advantage of the network topology to maximize thepacking of the activated links at each time instant.

In order for the TPs to have a knowledge of the channel gains, whichwere defined and discussed above with reference to FIG. 5 and Equation(1), the UEs will periodically send feedback information about theirlocal channel gains to the TPs according to some aspects. In someaspects, each UE measures the received signal power from one or morenearby dominant TPs. In some aspects, each UE will also measure theresidual interference caused by the remaining non-dominant TPs withinthe area 505. The measurements can be done, for example, via orthogonalpilot symbols.

In aspects, each UE can estimate the signal-to-interference-plus-noiseratio (SINR) due to the dominant TPs, while with respect to non-dominantTPs only interference is taken into account. Each UE will providefeedback on the measured SINR from the dominant TPs to the centralscheduler 504. Based on the received CQI feedback, the TPs decide whichUEs to serve, and thereafter perform UE scheduling for those UEs. TPsare also activated or deactivated according to procedures describedearlier herein based on the feedback. The measuring and reporting can bedone for wideband scheduling schemes as well as sub-band-levelscheduling schemes.

Each UE will only feedback the CQI corresponding to a select few nearbyTPs, referred to as the UE report set. The information-theoretic linkscheduling (ITLinQ) criterion is used to decide which TPs constitute theUE report set in some example aspects, although aspects are not limitedthereto. In particular, suppose that for each UE_(j), j∈{1, . . . , K},the UE or central scheduler 504 can identify the TP from which thestrongest reference signal received power (RSRP) is received, and denotethis TP by TP_(i) _(j) . In particular, if the RSRP of TP_(i) at UE_(j)is denoted by RSRP_(ji), then:

$\begin{matrix}{i_{j} = {\arg\;{\max\limits_{i \in {\{{1,\;\ldots\;,\; N}\}}}{{RSRP}_{ji}}}}} & (17)\end{matrix}$

After identifying the strongest TP for UE_(j), any TP_(i) will be in thereport set of UE_(j), denoted by C_(j), if and only if

$\begin{matrix}{\frac{{RSRP}_{ji}}{\sigma^{2}} \geq \left( \frac{{RSRP}_{{ji}_{j}}}{\sigma^{2}} \right)^{\eta_{CQI}}} & (18)\end{matrix}$for some η_(CQI)∈[0,1] that can be tuned to change the average reportset size throughout the network. The above criterion, which is based onthe ITLinQ scheduling criterion, states that the signal power due toTP_(i) at UE_(j) is “strong enough” to be included in the report set. Insome aspects, this parameter can be tuned such that the average size ofthe report sets of the UEs is around 4.

For each UE, the TPs outside the UE's report set are viewed as allcontributing toward an aggregate residual interference plus noise. Inparticular, for UE_(j), the residual interference at scheduling intervalt can be written as:δI _(j)(t)=Σ_(i∈{1, . . . ,N}\C) _(j) Pγ _(i)(t)|h _(ji)(t)|²+σ²  (19)where γ_(i)(t)∈{0,1} denotes the indicator variable representing whetheror not TP_(i) is active in scheduling interval t. It can be assumed thatfor each UE, the UE, central scheduler 504 or other element have accessto an estimate of the residual interference only based on the RSRPs. Inparticular, the following information is assumed to always be accessiblefor each UE:ΔI _(j)=Σ_(i∈{1, . . . ,N}C) _(j) RSRP_(ji)+σ²  (20)

Moreover, at each scheduling interval, each UE_(j) can obtain along-term average estimate of its received residual interference using,for example, the following recursive equationδ I _(j)(t)=α_(CSI) δI _(j)(t−1)+(1−α_(CSI))δI _(j)(t)  (21)where

$\alpha_{CSI} = {1 - \frac{1}{T_{FB}}}$is a constant determining the forget factor of the long-term residualinterference, which depends on the CQI feedback period T_(FB).

As mentioned above, CQI feedback period is denoted by T_(FB). Inaspects, there may also be a feedback delay denoted by Δ_(FB). These twoquantities will specify the frequency of the feedback reports and theirdelay in terms of the number of scheduling intervals.

For each n=1, 2, . . . , each UE_(j) can report the following CQIfeedback reports to the central schedule r504:

$\begin{matrix}{{{g_{ji}\left( {{nT}_{FB} + \Delta_{FB}} \right)} = \frac{P{{h_{ji}\left( {nT}_{FB} \right)}}^{2}}{{\overset{\_}{\delta\; I}}_{j}\left( {nT}_{FB} \right)}},{i \in C_{j}}} & (22)\end{matrix}$

This implies that at each scheduling interval whose index is a multipleof the feedback period T_(FB), the UE measures the received powers fromthe TPs in its report set and calculates the SINR due to each of thoseTPs considering the long-term residual interference at that schedulinginterval. Due to the processing delays at the transmitters andreceivers, the UE then reports such SINR information back to the centralscheduler 504 after a delay of Δ_(FB) scheduling intervals.

Aside from the above CQI feedback reports from the UEs, due to thecentralized scheduling process, we assume that for all the TPs in thenetwork, we have access to an estimate of the probability that the TP isgoing to be scheduled in each scheduling interval. In particular, foreach TP_(i), we denote this probability as γ _(i)(t) and estimate it as

$\begin{matrix}{{{\overset{\_}{\gamma}}_{i}(t)} = {{\frac{1}{t - 1}{\sum\limits_{t^{\prime} = 1}^{t - 1}{\gamma_{i}\left( t^{\prime} \right)}}} = \frac{{\left( {t - 2} \right){{\overset{\_}{\gamma}}_{i}\left( {t - 1} \right)}} + {\gamma_{i}\left( {t - 1} \right)}}{t - 1}}} & (23)\end{matrix}$

This equation implies that the scheduling probability of each TP isestimated as the average fraction of the scheduling intervals that theTP has been scheduled so far.

Having access to the above CQI information, especially the feedbackreports from the UEs, interference management as described earlierherein can be implemented at the central scheduler 504, for example,although aspects are not limited thereto. For example, rate and PF ratioestimation can be performed using feedback reports and CQI information.As described with reference to Equation (2), the instantaneous rate foreach UE is used to estimate the PF ratio for the UE, and the PF ratio inturn is used to associate UEs to the TPs according to UE schedulingalgorithms. PF ratio is used also for priority assignments according toITLinQ schemes.

FIG. 9 illustrates rate and proportional-fairness ratio estimationaccording to some aspects. Suppose that UE₁ has three TPs (TP1, TP2, andTP3) in its report set C₁={1,2,3} and TP4 and TP5 only contribute to theresidual interference at this UE. To estimate the instantaneous ratethat the UE is receiving from TP₁ at scheduling interval t, Equation (3)is solved according to the below:

$\begin{matrix}{{{\overset{\sim}{r}}_{11}(t)} = {\log_{2}\left( {1 + \frac{P{{h_{11}(t)}}^{2}}{{\delta\;{I_{1}(t)}} + {\sum_{i \in {C_{1}\backslash{\{ i\}}}}{P\;{\gamma_{i}(t)}{{h_{1i}(t)}}^{2}}}}} \right)}} & (24) \\{\mspace{59mu}{= {\log_{2}\left( {1 + \frac{\frac{P{{h_{11}(t)}}^{2}}{\delta\;{I_{1}(t)}}}{1 + {\sum\limits_{i = 2}^{3}{{\gamma_{i}(t)}\frac{P{{h_{1_{i}}(t)}}^{2}}{\delta\;{I_{1}(t)}}}}}} \right)}}} & (25) \\{\mspace{59mu}{\approx {\log_{2}\left( {1 + \frac{g_{11}(t)}{1 + {\sum\limits_{i = 2}^{3}{{{\overset{\_}{\gamma}}_{i}(t)}{g_{1_{i}}(t)}}}}} \right)}}} & (26)\end{matrix}$where in (26) the approximation

${g_{1i}(t)} \approx \frac{P{{h_{1_{i}}(t)}}^{2}}{\delta\;{I_{1}(t)}}$was used and for notational simplicity, if t≠nT_(FB)+Δ_(FB) for any n,we let g_(ji)(t):=g_(ji)(nT_(FB)+Δ_(FB)) where n=max m such thatmT_(FB)+Δ_(FB)<t which represents the most recent CQI feedback for thelink between TP_(i) and UE_(j).

Moreover, because it cannot be known a priori whether the interferingTPs in the dominant set (e.g., TP2 and TP3 in the above example) aregoing to be scheduled in the current scheduling interval, interferencepower for those TPs is scaled by a factor that is the probability ofthose TPs being scheduled in the current scheduling interval.

As described earlier herein NOMA can be used to serve more UEs at thesame time, enhancing both the system throughput and network coverage.CQI feedback reports can be used in NOMA implementations to estimate thepower allocation variables for a TP that is serving two users via NOMA.

FIG. 10 illustrates NOMA power allocation using CQI feedback reportsaccording to some aspects. Referring to FIG. 10, suppose TP₁ intends toserve UE₁ and UE₂ via NOMA. As a shorthand notation, let I_(D) _(j)denote the total interference to UE_(j) stemming from the interferingTPs in the UE_(j) report set. If the weight of each UE_(j) is denoted byw_(j), then the NOMA power allocation variable (discussed with referenceto Equation (6)) can be written as:

$\begin{matrix}{\beta^{*} = {1 - \frac{{w_{2}{h_{21}}^{2}\left( {I_{D_{1}} + {\delta\; I_{1}}} \right)} - {w_{1}{h_{11}}^{2}\left( {I_{D_{2}} + {\delta\; I_{2}}} \right)}}{P{h_{11}}^{2}{h_{21}}^{2}\left( {w_{1} - w_{2}} \right)}}} & {{~~~~}(27)} \\{= {1 - \frac{{w_{2}\frac{P{h_{11}}^{2}}{\delta\; I_{2}}\left( {1 + \frac{I_{D_{1}}}{\delta\; I_{1}}} \right)} - {w_{1}\frac{P{h_{11}}^{2}}{\delta\; I_{1}}\left( {1 + \frac{I_{D_{2}}}{\delta\; I_{2}}} \right)}}{\left( {w_{1} - w_{2}} \right)\left( \frac{P{h_{11}}^{2}}{\delta\; I_{1}} \right)\left( \frac{P{h_{21}}^{2}}{\delta\; I_{2}} \right)}}} & {{~~~~}(28)} \\{\approx {1 - \frac{{w_{2}{g_{21}\left( {1 + {\sum\limits_{i \in {C_{1}\backslash{\{ 1\}}}}^{\;}{{\overset{\_}{\gamma}}_{i}g_{1\; i}}}} \right)}} - {w_{1}{g_{11}\left( {1 + {\sum\limits_{i \in {C_{2}\backslash{\{ 1\}}}}^{\;}{{\overset{\_}{\gamma}}_{i}g_{2\; i}}}} \right)}}}{\left( {w_{1} - w_{2}} \right)g_{11}g_{21}}}} & {{~~~~}(29)}\end{matrix}$

As described earlier herein, interference management schemes canidentify a subset of links throughout the network 500 that can beactivated together with a minimal level of interference on each other,while the rest of the links are deactivated. For example, an ITLinQscheme can be used to decide which links can be turned on in thenetwork. FIG. 11 demonstrates how checking the ITLinQ schedulingconditions can be done using CQI feedback reports according to someaspects.

Referring to FIG. 11, suppose TP₁ is scheduled to serve UE₁ and thecentral scheduler 504 or other apparatus is to decide whether TP₂ shouldalso be activated to serve UE₂. Based on the ITLinQ criterion, TP₂should be activated if, for example, INR₁₂≤M·SNR₂ ^(η), which indicatesthat TP2 is not causing strong interference at higher-priority UEs thatare already scheduled. This inequality can make use of CQI feedbackreports by re-writing as below:

$\left. \leftrightarrow{\frac{P{h_{12}}^{2}}{\sigma^{2}} \leq {M\left( \frac{P{h_{22}}^{2}}{\sigma^{2}} \right)}^{\eta}}\leftrightarrow{{\frac{P{h_{12}}^{2}}{\delta\; I_{1}} \cdot \frac{\delta\; I_{1}}{\sigma^{2}}} \leq {M\left( {\frac{P{h_{22}}^{2}}{\delta\; I_{2}} \cdot \frac{\delta\; I_{2}}{\sigma^{2}}} \right)}^{\eta}}\leftrightarrow{{g_{12}\frac{\Delta\; I_{1}}{\sigma^{2}}} \lesssim {M\left( {g_{22}\frac{\Delta\; I_{2}}{\sigma^{2}}} \right)}^{\eta}} \right.$where

$\begin{matrix}{{{INR}_{ji}(t)} = \frac{P{{{\overset{\sim}{g}}_{ji}(t)}}^{2}}{\sigma^{2}}} & (30)\end{matrix}$

Therefore, using the CQI feedback reports and the long-term estimate ofthe residual interference at each UE, the ITLinQ scheduling conditionscan be verified or at least approximated.

After link scheduling is performed, the central scheduler 504 is awareof which TPs are being activated at the current scheduling interval andmore accurate estimate of the achievable rate for each UE can bederived. Referring again to FIG. 9, the instantaneous rate estimate thatthe UE is receiving from TP₁ can now be refined as

$\begin{matrix}{{{\overset{\sim}{r}}_{11}(t)} = {\log_{2}\left( {1 + \frac{P{{h_{11}(t)}}^{2}}{{\delta\;{I_{1}(t)}} + {\sum\limits_{i = 2}^{3}{P\;{\gamma_{i}(t)}{{h_{1i}(t)}}^{2}}}}} \right)}} & {{~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}(31)} \\{= {\log_{2}\left( {1 + \frac{\frac{P{{h_{11}(t)}}^{2}}{\delta\;{I_{1}(t)}}}{1 + {\sum\limits_{i = 2}^{3}{{\gamma_{i}(t)}\frac{P{{h_{1i}(t)}}^{2}}{\delta\;{I_{1}(t)}}}}}} \right)}} & {(32)} \\{\approx {\log_{2}\left( {1 + \frac{g_{11}(t)}{1 + {\sum\limits_{i = 2}^{3}{{\gamma_{i}(t)}{g_{1\; i}(t)}}}}} \right)}} & {(33)}\end{matrix}$where, compared to the rate estimate at the beginning of the schedulinginterval, the central scheduler 504 or other apparatus has the exactbinary knowledge of γ₂(t) and γ₃(t), which helps improve the rateestimation.

CQI reports can also be used to help adjust the transmission powers ofthe scheduled TPs. Using the aforementioned CQI reports, following theoptimization approach described earlier herein with reference toEquation (5) the transmit power of TP_(i), denoted by P*_(i), is in factthe solution to the following optimization problem:

${\max\limits_{P_{i}}{\sum\limits_{j = 1}^{i - 1}{w_{j}{\log\left( {1 + \frac{P_{j}^{*}g_{jj}}{\begin{matrix}{{P_{i}g_{ji}1\left( {i \in C_{j}} \right)} + {\sum\limits_{k \in {{\{{1,\ldots,{i - 1}}\}}\bigcap{C_{j}\backslash{\{ j\}}}}}^{\;}{P_{k}^{*}g_{jk}}} +} \\{{\sum\limits_{k \in {{\{{{i + 1},\ldots,n}\}}\bigcap C_{j}}}^{\;}{Pg}_{jk}} + P}\end{matrix}}} \right)}}}} + {w_{i}{\log\left( {1 + \frac{P_{i}g_{ii}}{{\sum\limits_{j \in {{\{{1,\ldots,{i - 1}}\}}\bigcap C_{i}}}^{\;}{P_{j}^{*}g_{ij}}} + {\sum\limits_{j \in {{\{{{i + 1},\ldots,n}\}}\bigcap C_{i}}}^{\;}{Pg}_{ij}} + P}} \right)}} + {\sum\limits_{j = {i + 1}}^{n}{w_{j}{\log\left( {1 + \frac{{Pg}_{jj}}{{P_{i}g_{ji}1\left( {i \in C_{j}} \right)} + {\sum\limits_{k \in {{\{{1,\ldots,{i - 1}}\}}\bigcap C_{j}}}^{\;}{P_{k}^{*}g_{jk}}} + {\sum\limits_{k \in {{\{{{i + 1},\ldots,n}\}}\bigcap{C_{j}\backslash{\{ j\}}}}}^{\;}{Pg}_{jk}} + P}} \right)}}}$where 1(⋅) denotes the indicator function and time indexing is omittedfor brevity. Note that adjusting the transmit powers of the TPs indownlink will also impact the CQI measurements and feedback reports. Tosignal the power allocations in downlink, dedicated reference signalscan be used for detection per sub-channel, or the power controldecisions can be signaled through the control channel. Specifically, theinterference measurements and the probabilities of the TPs in thenetwork being turned on may need to be modified based on the adjustedtransmission power levels of the TPs across the network.

As the TPs do not have access to the instantaneous CQI, TP estimates ofthe instantaneous rates may be optimistic or pessimistic, resulting inpacket drops when the estimated rate is higher than the capacity. Inorder to reduce or eliminate packet drops, systems according to aspectsuse an outer loop link adaptation scheme based on an ACK/NACK(acknowledgement/negative acknowledgement) feedback by the UE indicatingwhether the transmission was successful or not at each schedulinginterval.

The estimated SINR of a generic UE using the CQI feedback reports can bedenoted by SINR_(FB). The adjusted SINR that the TP will use fortransmission rate calculation is denoted by

SINR and is derived as

(dB)=SINR_(FB)(dB)+Δ_(OLLA)  (34)where Δ_(OLLA) is the OLLA adjustment of the SINR in dB.

The value of Δ_(OLLA) will evolve over time, and at each schedulinginterval t, its value is updated as follows:

$\begin{matrix}{{\Delta_{OLLA}(t)} = \left\{ \begin{matrix}{{\Delta_{OLLA}(t)} + {\alpha\;{\delta(t)}}} & {{if}\mspace{14mu}{ACK}} \\{{\Delta_{OLLA}(t)} - {\left( {1 - \alpha} \right){\delta(t)}}} & {{if}\mspace{14mu}{NACK}}\end{matrix} \right.} & (35)\end{matrix}$where α is the desired packet error rate (usually taken to be 0.1) andδ(t) is the adjustment step size, which can be reduced over time forconvergence purposes. This implies that if the last transmission wassuccessful, the SINR and rate estimates would become more aggressive,whereas if the last transmission failed, the SINR and rate estimateswould become more conservative. Note that such an update is done on aper-user basis in general, while if dynamic cell selection (DCS) isinvolved as described above with reference to FIG. 7 and associatedequations, then the update should be done per TP-UE link.Interference Management in Ultra-Dense Networks with Sub-Band Scheduling

Methods and apparatuses according to some aspects manage interference inultra-dense networks at the sub-band level.

Returning again to FIG. 5, it can be assumed that there are multipleresource blocks (RBs) across the frequency domain, each experiencing anindependent fading level. The RBs are assumed to be partitioned intodifferent sub-bands, with each sub-band including a set of adjacent RBs.Scheduling decisions can be done at the sub-band level. g_(ji)(t, f)denotes the channel gain between TP_(i) and UE_(j) at schedulinginterval t and sub-band f, consisting of both long-term components(corresponding to path-loss and shadowing) and short-term components(corresponding to short-term fading). This implies that at eachscheduling interval t, assuming synchronous transmission of the TPs, thereceived signal of UE_(j) on sub-band f can be written:Y _(j)(t,f)=Σ_(i=1) ^(N) g _(ji)(t,f)X _(i)(t,f)+Z _(j)(t,f)  (36)where X_(i)(t, f) denotes the transmit signal of TP_(i) at schedulinginterval t and sub-band f and Z_(j)(t, f) denotes the additive whiteGaussian noise at UE_(j) in scheduling interval t and sub-band f.

Channel gain can be estimated and is denoted by {tilde over (g)}_(ji)(t,f), and this estimate can be measured and fed back periodically (or uponnetwork request) by the UEs to the network. As an example, a CQIfeedback mechanism as described above can be implemented by UEs in thewireless network 500, and such feedback can be implemented on aper-sub-band basis. Note that the sub-band size for the CQI feedbackreports might be different from the size of sub-bands for the purpose ofscheduling. The set of RBs corresponding to the scheduling sub-bandf_(sch) is denoted by RBs(f_(sch)) and the set of RBs corresponding tothe CQI sub-band f_(CQI) by RBs(f_(CQI)). Then, for any pair (TP_(i),UE_(j)) and any scheduling sub-band f_(sch), if there exists a CQIsub-band f_(CQI) such that RBs(f_(sch))⊆RBs(f_(CQI)), then {tilde over(g)}_(ji)(t, f_(sch))={tilde over (g)}_(ji)(t, f_(CQI)). Otherwise,there should exist a set of CQI sub-bands {f_(CQI,1), . . . , f_(CQI,M)}such that RBs(f_(sch))=∪_(m=1) ^(M)RBs(f_(CQI,m)). In the latter case,TP_(i) can calculate an effective CQI on all of these bands to obtain aunique CQI value for the desired scheduling sub-band. One type ofeffective CQI calculation, for example, can be the following:

$\begin{matrix}{{{\overset{\sim}{g}}_{ji}\left( {t,f_{sch}} \right)} = {2^{({\frac{1}{M}{\sum\limits_{m = 1}^{M}{\log_{2}{({1 + {{\overset{\sim}{g}}_{ji}{({t,f_{{CQI}_{m}}})}}})}}}})} - 1}} & (37)\end{matrix}$which considers averaging in the log-domain and then converting back tothe actual domain.

Having access to the CQI values for each scheduling sub-band, userscheduling and link scheduling can be implemented at the centralscheduler 504 or other apparatus as described below.

As with other aspects described above, each TP maintains a list of theUEs associated with it, denoted by L_(i). In particular, if PL_(ji)denotes the combined path-loss and shadowing between TP_(i) and UE_(j),then

$\begin{matrix}{L_{i} = \left\{ {{{j \in \left\{ {1,\ldots\mspace{14mu},K} \right\}}:i} = {\arg\;{\max\limits_{i^{\prime}}{PL}_{{ji}^{\prime}}}}} \right\}} & (38)\end{matrix}$

Without loss of generality, it can be assumed that each TP has at leastone UE associated with it (for all i∈{1, . . . , N}, |L_(i)|≠0). TPswith no assigned UEs will be dropped in further operations as needed andas described below. As described earlier herein with respect to Equation(2), the UE list of each TP is ordered according to a given prioritycriterion, for example proportional fairness (PF). On a sub-band levelaccording to aspects, at the start of scheduling interval t, the PFratio for each UE_(j) on sub-band f as

$\begin{matrix}{{PF}_{j,f} = \frac{{\overset{\sim}{r}}_{j}\left( {t,f} \right)}{{\overset{\_}{R}}_{j}\left( {t - 1} \right)}} & (39)\end{matrix}$where {tilde over (r)}_(j)(t, f) denotes the estimated instantaneousrate that UE_(j) receives from TP_(i) _(j) in the scheduling interval tand sub-band f.

Each TP_(i) forms an |L_(i)|×F matrix PF_(i) that includes the PF ratiosof its associated users across all F scheduling sub-bands. For example,the (j, f)^(th) element of this matrix corresponds to the PF ratio ofUE_(j) on sub-band f. After forming this matrix, TP_(i) can pick themaximum element of PF_(i). Let the row and column of the maximum entrybe denoted by (j₁, f₁). Then, the TP can schedule UE_(j) ₁ on sub-bandf₁. Then, TP_(i) can set all PF values in the f₁ ^(th) column of the PFmatrix PF_(i) to zero (PF_(i)(j, f₁)=0, ∀j∈L_(i)), indicating thatsub-band f₁ has already been occupied. Moreover, in case of non-fullbuffer traffic, the PF ratio of user UE_(j) ₁ on the unoccupiedsub-bands may be updated to reflect the number of bits remaining in aprospective buffer that the TP maintains for all of the TP's associatedUEs. The TP can then select the next maximum value of the updated PF_(i)matrix, schedule the corresponding UE on the corresponding sub-band,update the matrix and so on. This process continues until all elementsof the PF matrix are zero, indicating that either the buffers of the UEsare perceived to be completely empty, or all bands have been occupiedwith scheduled users.

FIGS. 12A, 12B, 12C and 12D illustrate the user scheduling processdescribed above according to some aspects. FIG. 12A illustrates aproportional fairness (PF) matrix at the beginning of a user schedulingprocess according to some aspects. In the illustrated example, threesub-bands f1, f2 and f3 are available. Four UEs UE1, UE2, UE3 and UE4are associated to the TP. PF values are provided for each entry. UE4 onsub-band f1 has the highest PF entry in the illustrated example.Accordingly, the TP will schedule UE4 on sub-band f1 and zero outsub-band f1 indicating that sub-band f1 is occupied.

FIG. 12B illustrates a PF matrix after scheduling of one user accordingto some aspects. As can be seen in the illustrated example, sub-band f1is unavailable. UE3 on sub-band f3 now has the highest PF. Accordingly,the TP will schedule UE3 on sub-band f3 and zero out sub-band f3indicating that sub-band f3 is occupied.

FIG. 12C illustrates a PF matrix after scheduling of two users accordingto some aspects. As described above, UE3 and UE4 have been scheduled,and f1 and f3 are occupied and no longer available. UE3 on sub-band f2now has the highest PF. Because a UE can be scheduled on more than onesub-band, the TP will schedule UE3 on sub-band f2 and zero out sub-bandf2 indicating that sub-band f2 is occupied.

FIG. 12D illustrates a PF matrix after scheduling of three usersaccording to some aspects. As can be appreciated by studying FIG. 12D,no sub-bands are available. At this point, user scheduling has beencompleted until the next scheduling interval.

User scheduling shown in FIGS. 12A, 12B, 12C and 12D was sequential overall of the sub-bands, but other scheduling methodologies can becontemplated and aspects are not limited to sequential scheduling.

Each TP can also schedule two UEs at the same time and use superpositioncoding to send desired messages to both the primary and secondary UEs.The primary UE will treat the message of the second UE as noise, whilethe secondary UE first decodes and subtracts the message of the first UEto decode its own messages. This approach was described earlier hereinwith reference to Equations (8) and (9).

For the case of sub-band user scheduling with NOMA, each TP can scheduleprimary UEs as described above. After the primary UEs are selected, theTPs will search for potential secondary NOMA UEs for whom scheduling canenhance the weighted sum-rate of the scheduler outcome. In particular,let (j₁, . . . , j_(i)) denote the tuple of primary scheduled UEs byTP_(i), where their scheduling order based on their respective PFs ispreserved. Moreover, assume that each UE j_(i′) has been scheduled on asubset of sub-bands denoted by F_(i′). In some aspects, the NOMA userpairings can be such that the pairings on different sub-bands are eithercompletely identical or disjoint.

To that end, TP_(i) starts by considering UE_(j) ₁ as the primary UE andcan search for a potential secondary NOMA UE_(j′) ₁ , j′₁∈L_(i)\{j₁, . .. , j_(i)} to be scheduled on all sub-bands in F₁. In particular, foreach j′₁∈L_(i)\{j₁, . . . , j_(i)}, the TP can determine whether eitherUE_(j) ₁ or UE_(j′) ₁ is the stronger UE in all sub-bands in F₁ in termsof CQI. If so, then the TP derives optimal NOMA power allocationvariables β_(f) as described above with reference to Equations (8) and(9) for each sub-band f∈F₁. The TP then determines whetherβ_(f)∈[0.5,1], for each f∈F₁. If so, then UE_(j′) ₁ is a potential NOMAUE for sub-bands F₁ with the NOMA power allocation variable

${\beta_{j_{1}^{\prime}} = {\frac{1}{F_{1}}{\sum\limits_{f \in F_{1}}^{\;}\beta_{f}}}},$which by definition is also between 0.5 and 1. The TP can perform theseoperations for all candidate NOMA UE and then compare the maximum NOMAweighted sum-rate among the candidate NOMA UEs and with the single-userweighted sum-rate of UE_(j) ₁ across all sub-bands in F₁. If NOMA helpsimprove the weighted sum-rate, then the TP may switch to NOMA across allsub-bands in F₁. Otherwise, the TP may remain in the single-user mode.

Subsequent to the user scheduling described above, the network 500 willinclude N TPs, each with one or two scheduled UE pairs on each of the Fsub-bands. Link scheduling (e.g., determining whether a given TP shouldbe activated) can proceed according to aspects using any of thealgorithms described in the disclosure, on each sub-band in parallel. IfNOMA had also been involved in the user scheduling phase, after the linkscheduling is done, the NOMA power allocation variables can be updatedafter link scheduling is completed, based on the resultant pattern ofinterference on different sub-bands across the network 500.

User scheduling and link scheduling can be performed together in amethod 1300 similar to that illustrated in FIG. 13. As seen at operation1302, the dynamic cell selection (DCS) algorithm described earlierherein (FIG. 7 and associated text) has been generalized to include thecase where multiple sub-bands are available and a list is maintained(by, e.g., the central scheduler 504) of available TPs and a list ofavailable UEs for all F scheduling sub-bands that are available.

The sub-band DCS algorithm shown in FIG. 13 can also be considered as anextension of the sub-band scheduling algorithm mentioned above withreference to FIGS. 12A, 12B, 12C and 12D, with an added dimension to thelocal user priority matrices maintained by each TP as depicted atoperation 1304. In the DCS algorithm of FIG. 13, the central scheduler504 estimates a 3-dimensional K×F×N priority matrix for the UEs acrossthe network 500, e.g., using the PF ratios. For any tuple (j, f, i)∈{1,. . . , K}×{1, . . . , F}×{1, . . . , N}, the (j, f, i)^(th) entry ofthis matrix represents the PF ratio of UE_(j) on sub-band f if served byTP_(i). This provides the freedom for the UEs to be served by anyavailable TP in the network 500 on any of the sub-bands.

Method 1300 continues with operation 1306 by selecting the (UE,sub-band, TP) tuple whose corresponding entry has the highest PF ratioin the entire PF matrix of the network 500. This and other operations ofmethod 1300 can be performed centrally, for example by processingcircuitry 502 of central scheduler 504.

Method 1300 continues with operation 1308 with the central scheduler 504checking whether the selected TP-UE pair should be scheduled on theselected sub-band. The decision can be based on any link schedulingcriterion, such as ITLinQ. If the link scheduling criteria are notsatisfied, either the TP or the UE will be removed from the selectedsub-band in operation 1310. Otherwise, the pair will be scheduled on theselected sub-band, and the TP and UE will be respectively removed fromthe set of available TPs and the set of available UEs on the selectedsub-band in operation 1312. The unavailability of the scheduled TP onthe selected sub-band may affect other UEs which are not yet scheduledon the selected sub-band. Therefore, the rest of the UEs which wereassociated with the selected TP should be associated with the nextstrongest available TP on the selected sub-band and their PF ratios alsoneed to be updated in operation 1314. The algorithm then continues inthe same fashion to select the next best (UE, sub-band, TP) tuples untilwe run out of either available TPs or available UEs on all thesub-bands.

For the case of non-full buffer traffic, the scheduler maintains aprospective buffer size for all the UEs, which gets updated as the UEsare being scheduled on the sub-bands. This implies that during thescheduling process, the PF ratio of any scheduled UE may be modified inthe yet-to-be-scheduled sub-bands to reflect the size of the remainingbits in its prospective buffer.

The method 1300 can be expanded to include checking of interferencecriteria such as ITLinQ criteria. In at least these aspects, afterselecting the best (UE, sub-band, TP) tuple in each iteration inoperation 1306, the central scheduler 504 can check whether the selectedUE is receiving strong interference from the TPs that have already beenscheduled on the selected sub-band. If so, the UE will be removed fromthe selected sub-band. Otherwise, the central scheduler 504 can alsocheck whether the TP is causing strong interference at the UEs alreadyscheduled on the selected sub-band. If so, the TP will be removed fromthe selected sub-band. However, if all incoming and outgoinginterference levels are weak enough, then the TP-UE pair will bescheduled on the selected sub-band, and the algorithm continues to findthe next best (UE, sub-band, TP) tuples by iteratively repeatingoperations starting with operation 1306.

The method 1300 can also be expanded to include NOMA considerations. Inat least these example aspects, once single-UE scheduling is completed,the central scheduler 504 can go through each of the scheduled TP-UEpairs and search for a potential secondary NOMA UE that can help improvethe weighted sum-rate that the TP can achieve over all the scheduledsub-bands of this pair. The scheduling is complete after all pairs havebeen examined for a potential NOMA UE addition.

Decentralized Link Scheduling Using Multi-Agent Deep ReinforcementLearning

In some aspects, link scheduling can be performed in a decentralizedfashion. For example, rather than a central scheduler (e.g., centralscheduler 504 (FIG. 5)) deciding whether each TP should be on or off,the TPs themselves can make on/off decisions. In at least these aspects,TPs can implement algorithms based on deep Q networks (DQN) and policygradients (PG) to decide whether it should stay on or become silent.Link scheduling according to these accepts can be more scalable tonetworks using large numbers of TPs, relative to centralized linkscheduling.

In at least these aspects, each TP in the network operates as areinforcement learning agent, which interacts with the wireless networkenvironment, by having a local observation of the environment, takingactions (being on/off) and receiving a reward at each schedulinginterval. Methods according to aspects use the experiences of allreinforcement learning agents to tune the scheduling algorithm used byall the agents via deep reinforcement learning.

In some examples, each TP can have access to CQIs provided as feedbackby UEs as described earlier herein with reference to FIGS. 9-11 and theaccompanying text. Having access to the CQI values, each TP maintains alist of its associated UEs to which it has, for example, the highestlevels of reference received signal power (RSRP). L_(i) denotes the listof UEs associated with TP_(i). Then, at each scheduling interval, eachTP selects one of the UEs from its association list to serve at thecurrent interval. The UE scheduling criterion can be based on, forexample, a priority criterion for the UEs, such as proportional fairness(PF) as has been described previously with respect to other aspects.

After the user scheduling phase, the network 500 will include N TP-UEpairs. In such a network, some TPs should be turned off (e.g., enter anidle state, refrain from transmitting, or turn off a power switch) suchthat the rest of the transmissions incur minimal levels of interferenceon each other. To that end, some aspects provide a decentralizedapproach, where each TP receives a local observation of the wirelessnetwork at the current interval, based on which each TP should decidewhether it stays on/off without knowledge of what the rest of the TPsare doing. These and other aspects can use reinforcement learning (RL)paradigms.

The RL paradigm consists of an agent and an environment interacting witheach other. The agent takes actions over time, and each such actioninfluences the next state of the environment. Moreover, the environmentemits a reward to the agent once the action is taken, and transitions tothe next state, which can be observed by the agent to take the nextaction. The goal is for the agent to take actions so as to maximize itscumulative future reward.

Expressed mathematically, at each time step t, the agent observes states_(t), takes an action a_(t), and receives scalar reward r_(t). Theenvironment receives action a_(t), emits a scalar reward r_(t), andtransitions to state s_(t+1). The transitions of the environment stateis assumed to follow a Markov Decision Process (MDP).

The RL agent needs to learn a policy π, which is defined as a behaviorfunction mapping the state space to the action space. For example, ateach time step t, the agent takes the action a_(t)=π(s_(t)). The goal ofthe agent is to learn a policy which maximizes a value function, forexample, discounted cumulative reward defined as:Q ^(π)(s _(k))=Σ_(t=k) ^(∞)γ^(t-k) r _(t) =r _(k) +γr _(k+1)+γ² r_(k+2)+ . . .  (40)where γ is a discount factor in the interval [0,1).

One way to learn such an optimal policy is through a technique calledQ-learning. This technique assigns a function Q(s, a) to eachstate-action pair, which is defined as the maximum discounted cumulativereward when we perform action a in state s, and continue optimally fromthat point on:Q(s _(t) ,a _(t))=max Q ^(π)(s _(t))  (41)

Assuming the Q function is available, at each time step t, the agenttakes the action with the highest Q-value:

$\begin{matrix}{{\pi\left( s_{t} \right)} = {\arg\;{\max\limits_{a}{Q\left( {s_{t},a} \right)}}}} & (42)\end{matrix}$

To find the Q function however, the Bellman equation is solved:

$\begin{matrix}{{Q\left( {s,a} \right)} = {{r\left( {s,a} \right)} + {\gamma\;{\max\limits_{a^{\prime}}{Q\left( {s^{\prime},a^{\prime}} \right)}}}}} & (43)\end{matrix}$

The Bellman equation states that the maximum future reward for thecurrent state and action is the immediate reward plus maximum futurereward for the next state. In Equation (43), s′ denotes the next stateresulting from taking action a at state s. If the number of states arefinite and small, one can resort to a look-up table approach to learnthe Q function. However, if the number of states are infinite, one canuse, for example, a neural network (NN) to represent the Q function. Adeep Q-network (DQN) is a neural network taking the state as an inputand outputting the Q values for different actions given the input state.Such a neural network can be trained, through the past experiences ofthe agent, so as to predict the desired Q-values for any given statewith high precision. This extends the conventional reinforcementlearning to what is known as deep reinforcement learning, which is usedby TPs in some aspects for link scheduling.

In link scheduling according to some aspects, each of the N TPs isdefined to be an individual deep RL agent, interacting with the wirelessnetwork 500 (and accordingly with other TPs in the network 500). At eachscheduling interval t, it can be assumed that each of the N agents has alocal observation of the wireless network 500. In particular, it isassumed that at scheduling interval t, each agent i∈{1, . . . , N}receives a local observation o_(i,t) with joint observation probabilityP(o_(1,t), . . . , o_(N,t)|s_(t)), where s_(t) is the environment stateat scheduling interval t.

Based on its local observation o_(i), each agent i then executes actiona_(i,t)∈{0,1}, resulting in the joint action vector a_(t)=[a_(1,t), . .. , a_(N,t)], which represents the scheduling decisions of all the TPsacross the network 500.

Based on this joint action vector, each agent i receives a rewardr_(i,t)=R_(i)(s_(t), a_(t)). This indicates that the reward of eachagent may depend on the action of all agents. The joint action vectorcauses the environment to transition to state s_(t+1) with transitionprobability P(s_(t+1)|s_(t), a_(t)).

In aspects, the environment state is considered to include an N×N matrixof channel quality indicator (CQI) feedback reports from the scheduledUEs; long-term average rates of all UEs; and the probability of each TPbeing on (estimated from previous intervals)

Agent local observations of each agent i include signal-to-noise ratio(SNR) CQI between TP_(i) and its scheduled UE; CQI regarding the top-x(where x is any positive integer) incoming interfering links (receivedby the scheduled UE of TP_(i)) in descending order; CQI regarding thetop-x outgoing interfering links (caused by TP_(i)) in descending order;weight of the scheduled UE of TP_(i), defined for example as the inverseof its long-term average rate; and estimated link capacity betweenTP_(i) and its scheduled UE.

The scalability of link scheduling according to these aspects isenhanced by the fact that the size of agents' local observations isconstant, because the number of inputs to the DQN of some aspects doesnot scale with the network density. Moreover, the same DQN can still beused even if an agent joins/leaves the network 500.

The action space of each agent is assumed to be the scheduling decisionof its corresponding TP, or {0,1} (e.g., on/off), or any other set ofdiscrete transmit power levels. Rewards at each scheduling interval caninclude any function of the resulting throughput of the UEs such as theweighted sum-rate achieved by all the UEs across the network.

Rewards can also be decentralized in some aspects of deep RL scheduling.For example, in some aspects, the reward of each agent is the individualrate of its own scheduled UE (decentralized reward) as opposed to theweighted sum-rate across the entire network 500. In at least theseaspects, a policy gradient (PG) algorithm can be used. While the DQNapproach tries to learn a state-action value function (the Q-function),the PG approach tries to learn a (possibly stochastic) policy directlyinstead of a Q function that maximizes the expected reward usinggradient methods.

FIG. 14 illustrates a training mechanism for a network with a number ofpolicy gradient agents according to some aspects. For a 5×5 wirelessnetwork with 5 agents symbolized on the left side of FIG. 14, for eachinstance of the environment (dropping of different TPs and UEs), thenetwork 500 can be run for a number (e.g., 1000) scheduling intervals.At the end of each drop, as shown in the chart on the right side of FIG.14, the cumulative reward of agent i at scheduling interval t∈{201, . .. , 600} can be calculated according to

${\frac{1}{1001 - t}{\sum\limits_{t^{\prime} = t}^{1000}{R_{i}\left( t^{\prime} \right)}}},$which indicates the mean achieved rate starting from scheduling intervalt.Interference Management in V2X Communications

Some aspects also apply to V2X communications, particularly within theultra-dense networks described above. As the number of autonomous andconnected vehicles grows, the number of wireless connections on theroads will increase, increasing the simultaneous access to same channelsby thousands of vehicles. Autonomous and connected vehicles will beconstantly relying on connectivity (cellular, Wi-Fi, Bluetooth, etc.)for high-definition maps and other data-intensive services, and therewill also be an increase in machine-type (small data) communicationsvying for the same channels. Many vehicles on the market today alreadyprovide a Wi-Fi hotspot with a cellular backhaul for the passengers. Asthe number of connections (and devices) increases, there is greaterpotential for interference. Channel allocation becomes more important,and aspects provide for improved channel allocation that helps reduce oreliminate interference.

Aspects provide at least two solutions to channel allocation issues. Anetwork-assisted solution is provided that uses Multi-Access EdgeComputing (MEC), in which the computation is done at the network edgewith local information and reduced latency. Another solution, in someaspects, uses machine learning (ML) at the edge or in a distributedmanner by the vehicles or a hybrid model. Aspects using ML use historicdata to optimize the channel allocation based on aspects such astrajectory of the vehicles, road topology, applications types, channelallocation by other vehicles, and their radio resource requirements. Ahybrid solution using both MEC and ML is also provided in some aspects.

FIG. 15 illustrates machine learning-based multi-access edge computingaccording to some aspects. Aspects can make use of networkinfrastructure 1500 in channel selection. Local information is used toselect the optimized configuration for mobile BSSs 1502 (e.g.,vehicles). This information can be processed at the core, in the cloud1504, at the access network/network edge 1506, or a combination thereof.The information 1508 can include operating channel information collectedfrom vehicles; measured interference collected from vehicles; existingnetworks/occupied channels information; travel route of the vehicle;road topology; type of traffic to and from each vehicle (e.g.,information regarding data size, quality of service (QoS) requirements,etc.); expected duration of the service consumed by the vehicle; servicecategory (e.g., entertainment, etc.); and other types of information.

The determined configuration is communicated to the vehicles at 1510using the backhaul connection to the network and may include informationabout duration or locality of validity of the information. Configurationinformation can include at least identification information of thechannel/s to be used, aggregation information, transmit power, etc.

These and other aspects can be used alone or in conjunction withML-based solutions implement at network or infrastructure elements asshown at 1512. In ML-based solutions, historical information availablefrom vehicles, including at least the information described above, aswell as information available at the network, contextual information(e.g., expected congestion, predicted service consumption and roaddensity, etc.) are used to train network elements to predict an optimalchannel allocation. ML-based solutions can also be implemented at thevehicles (not shown in FIG. 15) to learn the optimal/correctconfiguration, channel, power, etc. for trips typically taken by therespective vehicle. Accordingly vehicles can make real-time decisions toswitch channels without input from network elements.

FIG. 16 illustrates a machine learning solution 1600 for channelallocation in a vehicle according to some aspects. Any of the aspectsshown can be implemented in the machine 1700 (FIG. 17) described laterherein or can make use of any component of FIGS. 1-4 as describedearlier herein. For example, data collection circuitry 1602 can collectrelevant data, including measured interference/noise on differentchannels, application bandwidth requirements, concurrent applications atthe vehicle leveraging similar or different interfaces, location,day/time, vehicle occupancy, spatial information, traffic info, etc. thedata collection circuitry 1602 can provide this data for storage at datastorage 1604. The data collection circuitry 1602 can also provided thisdata as input to the ML engine.

Using data from the data collection circuitry 1602 and the data storage1604, the ML engine 1606 can create a model for mobility trajectoryprediction, which predicts the route or other locational data forvehicles and other UEs. The ML engine 1606 can also predict bandwidthrequirements of vehicles and perform channel selection after makingpredictions based on the models created. The ML engine can providechannel selection or other inputs to the network configurator 1608,which implements the dynamic channel selection (with possible channelsaggregation according to services bandwidth requirement and category)and possibly other relevant network configurations.

In a hybrid solution, in some aspects, distributed ML processing isimplemented and the vehicles share the result of learning models withthe network, which uses that information as an input to its own model.This approach results in lower latency, more accuracy and a moreefficient solution at least because not all the contextual data will betransmitted to the network.

In an alternative solution, in some aspects, distributed ML processingis implemented in the vehicle and the vehicle can itself make a channelselection or other configuration change based on the learnedinformation. The vehicle shares can share channel expected status orother learned information throughout the rest of the current trip. Thenetwork can use the channel current status and predicted statusinformation to arbitrate other dynamic channel allocation in the sameroad segment.

Other Apparatuses

FIG. 17 illustrates a block diagram of an example machine 1700 uponwhich any one or more of the techniques (e.g., methodologies) discussedherein may perform. In alternative aspects, the machine 1700 may operateas a standalone device or may be connected (e.g., networked) to othermachines. In a networked deployment, the machine 1700 may operate in thecapacity of a server machine, a client machine, or both in server-clientnetwork environments. In an example, the machine 1700 may act as a peermachine in peer-to-peer (P2P) (or other distributed) networkenvironment. Further, while only a single machine is illustrated, theterm “machine” shall also be taken to include any collection of machinesthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methodologies discussedherein, such as cloud computing, software as a service (SaaS), othercomputer cluster configurations.

Examples, as described herein, may include, or may operate by, logic ora number of components, or mechanisms. Circuitry is a collection ofcircuits implemented in tangible entities that include hardware (e.g.,simple circuits, gates, logic, etc.). Circuitry membership may beflexible over time and underlying hardware variability. Circuitriesinclude members that may, alone or in combination, perform specifiedoperations when operating. In an example, hardware of the circuitry maybe immutably designed to carry out a specific operation (e.g.,hardwired). In an example, the hardware of the circuitry may includevariably connected physical components (e.g., execution units,transistors, simple circuits, etc.) including a computer readable mediumphysically modified (e.g., magnetically, electrically, moveableplacement of invariant massed particles, etc.) to encode instructions ofthe specific operation. In connecting the physical components, theunderlying electrical properties of a hardware constituent are changed,for example, from an insulator to a conductor or vice versa. Theinstructions enable embedded hardware (e.g., the execution units or aloading mechanism) to create members of the circuitry in hardware viathe variable connections to carry out portions of the specific operationwhen in operation. Accordingly, the computer readable medium iscommunicatively coupled to the other components of the circuitry whenthe device is operating. In an example, any of the physical componentsmay be used in more than one member of more than one circuitry. Forexample, under operation, execution units may be used in a first circuitof a first circuitry at one point in time and reused by a second circuitin the first circuitry, or by a third circuit in a second circuitry at adifferent time.

Machine (e.g., computer system) 1700 may include a hardware processor1702 (e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 1704 and a static memory 1706, some or all of which maycommunicate with each other via an interlink (e.g., bus) 1708. Themachine 1700 may further include a display unit 1710, an alphanumericinput device 1712 (e.g., a keyboard), and a user interface (UI)navigation device 1714 (e.g., a mouse). In an example, the display unit1710, alphanumeric input device 1712 and UI navigation device 1714 maybe a touch screen display. The machine 1700 may additionally include astorage device (e.g., drive unit) 1716, a signal generation device 1718(e.g., a speaker), a network interface device 1720.

The storage device 1716 may include a machine readable medium 1722 onwhich is stored one or more sets of data structures or instructions 1724(e.g., software) embodying or utilized by any one or more of thetechniques or functions described herein. The instructions 1724 may alsoreside, completely or at least partially, within the main memory 1704,within static memory 1706, or within the hardware processor 1702 duringexecution thereof by the machine 1700. In an example, one or anycombination of the hardware processor 1702, the main memory 1704, thestatic memory 1706, or the storage device 1716 may constitutemachine-readable media.

While the machine readable medium 1722 is illustrated as a singlemedium, the term “machine readable medium” may include a single mediumor multiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 1724.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 1700 and that cause the machine 1700 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples mayinclude solid-state memories, and optical and magnetic media. In anexample, a massed machine readable medium comprises a machine readablemedium with a plurality of particles having invariant (e.g., rest) mass.Accordingly, massed machine-readable media are not transitorypropagating signals. Specific examples of massed machine readable mediamay include: non-volatile memory, such as semiconductor memory devices(e.g., Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks, such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 1724 may further be transmitted or received over acommunications network 1726 using a transmission medium via the networkinterface device 1720 utilizing any one of a number of transferprotocols (e.g., frame relay, internet protocol (IP), transmissioncontrol protocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.6 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 1720 may include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas as discussedabove with reference to FIG. 3, to connect to the communications network1726. In an example, the network interface device 1720 may include aplurality of antennas to wirelessly communicate using at least one ofsingle-input multiple-output (SIMO), MIMO, or multiple-inputsingle-output (MISO) techniques. The term “transmission medium” shall betaken to include any intangible medium that is capable of storing,encoding or carrying instructions for execution by the machine 1700, andincludes digital or analog communications signals or other intangiblemedium to facilitate communication of such software.

EXAMPLES

Although an aspect has been described with reference to specific exampleaspects, it will be evident that various modifications and changes maybe made to these aspects without departing from the broader spirit andscope of the present disclosure. Accordingly, the specification anddrawings are to be regarded in an illustrative rather than a restrictivesense. The accompanying drawings that form a part hereof show, by way ofillustration, and not of limitation, specific aspects in which thesubject matter may be practiced. The aspects illustrated are describedin sufficient detail to enable those skilled in the art to practice theteachings disclosed herein. Other aspects may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. ThisDetailed Description, therefore, is not to be taken in a limiting sense,and the scope of various aspects is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such aspects of the inventive subject matter may be referred to herein,individually and/or collectively, by the term “aspect” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any single aspect or inventive concept if more than oneis in fact disclosed. Thus, although specific aspects have beenillustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific aspects shown. This disclosure is intended to cover anyand all adaptations or variations of various aspects. Combinations ofthe above aspects, and other aspects not specifically described herein,will be apparent to those of skill in the art upon reviewing the abovedescription.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In this document, the terms “including” and “inwhich” are used as the plain-English equivalents of the respective terms“comprising” and “wherein.” Also, in the following claims, the terms“including” and “comprising” are open-ended, that is, a system, UE,article, composition, formulation, or process that includes elements inaddition to those listed after such a term in a claim are still deemedto fall within the scope of that claim. Moreover, in the followingclaims, the terms “first,” “second,” and “third,” etc. are used merelyas labels, and are not intended to impose numerical requirements ontheir objects.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single aspect for the purpose of streamlining thedisclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed aspects require more featuresthan are expressly recited in each claim. Rather, as the followingclaims reflect, inventive subject matter lies in less than all featuresof a single disclosed aspect. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate aspect.

The following describes various examples of methods, machine-readablemedia, and systems (e.g., machines, devices, or other apparatus)discussed herein.

Example 1 includes subject matter (such as a device, an electronicapparatus (e.g. circuit, electronic system or both), or a machine)including memory to maintain a list of pairs of user equipments (UEs)and transmit points (TPs) within an area; and processing circuitrycoupled to the memory, the processing circuitry configured to designatean order for the list of pairs based upon a priority criterion, a firstpair of the list of pairs having a highest priority based on thepriority criterion; and provide a message to a first TP corresponding tothe first pair, the message including an instruction to adjusttransmission power to a first optimized power level based on anoptimization function.

In Example 2, the subject matter of Example 1 may optionally includewherein the processing circuitry is further configured to provide amessage to a second TP corresponding to a second pair of the list ofpairs, the second pair having a lower priority than the first pair, themessage including an instruction to adjust transmission power to asecond optimized power level based on the optimization function andbased on the first optimized power level.

In Example 3, the subject matter of Example 2 may optionally includewherein if the optimized power level is less than or equal to about 10%of full transmission power for a TP within the area, the processingcircuitry is configured to instruct the respective TP to turn off.

In Example 4, the subject matter of any of Examples 2-3 may optionallyinclude wherein the processing circuitry is configured to providemessages to TPs sequentially according to priority of a respective pairof the list of pairs.

In Example 5, the subject matter of any of Examples 1-4 may optionallyinclude wherein the priority criterion includes a proportional fairnesscriterion.

In Example 6, the subject matter of any of Examples 1-5 may optionallyinclude wherein the optimization function includes a weighted sum ratefunction.

In Example 7, the subject matter of Example 6 may optionally includewherein at least one pair of the list of pairs operates innon-orthogonal multiple access (NOMA), and wherein the order for thelist of pairs is based on a weighted NOMA sum rate.

In Example 8, the subject matter of Example 6 may optionally includewherein the optimization function is further based on actual channelgain between a TP of a pair of the list of pairs and a respective UE ofthe pair.

In Example 9, the subject matter of any of Examples 1-8 may optionallyinclude transceiver circuitry and wherein the processing circuitry iscoupled to the transceiver circuitry and configured to provide themessage over a control channel using the transceiver circuitry.

Example 10 includes subject matter (such as a device, an electronicapparatus (e.g. circuit, electronic system or both), or a machine)comprising memory to maintain a list of user equipments (UEs) andtransmit points (TPs) within an area; and processing circuitry coupledto the memory, the processing circuitry configured to associate each UEof the list of UEs with a serving TP of the list of TPs based on a valuerepresentative of signal strength between each UE and the serving TP;determine, for each UE of the list of UEs, and sequentially in an orderbased on priority of each UE of the list of UEs, whether the respectiveserving TP is to be activated; and provide a message to the respectiveserving TP based on whether the respective serving TP is to beactivated.

In Example 11, the subject matter of Example 10 may optionally includewherein the value representative of channel strength includes long-termchannel gain.

In Example 12, the subject matter of any of Examples 10-11 mayoptionally include wherein the processing circuitry is furtherconfigured to perform the associating periodically and according to ascheduling interval.

In Example 13, the subject matter of Example 12 may optionally includewherein the processing circuitry is configured to determine whether aserving TP is to be activated based on at least one of an interferencelevel generated by the respective serving TP and an interference levelreceived by the scheduled UE.

In Example 14, the subject matter of Example 12 may optionally includewherein the processing circuitry is configured to remove a UE from thelist of UEs, for at least the scheduling interval, if interference seenat the UE is above a threshold.

In Example 15, the subject matter of Example 12 may optionally includewherein the processing circuitry is configured to remove a TP from thelist of TPs, for at least the scheduling interval, if interferenceproduced by the TP is above a threshold.

Example 16 includes subject matter (such as a device, an electronicapparatus (e.g., circuit, electronic system, or both), or a machine)including transmit circuitry; receive circuitry to measure receivedsignal power from one or more dominant transmit points (TPs); andprocessing circuitry coupled to the transmit circuitry and to thereceive circuitry and configured to provide estimatedsignal-to-interference-plus-noise ratios (SINRs) relevant to each of theone or more dominant TPs; and encode feedback information indicative ofthe estimated SINRs for transmission to a central scheduler.

In Example 17, the subject matter of Example 16 may optionally includewherein the processing circuitry is further configured to determinemembers of a report set of TPs based on reference signal received power(RSRP) for one or more TPs.

In Example 18, the subject matter of any of Examples 16-17 mayoptionally include wherein the receive circuitry is further configuredto measure aggregate interference caused by one or more non-dominantTPs.

In Example 19, the subject matter of any of Examples 16-18 mayoptionally include wherein the processing circuitry is furtherconfigured to report the feedback information periodically according toa periodicity that is based on a scheduling interval within a wirelesscommunication network including at least the apparatus and a report setof TPs.

Example 20 includes subject matter (such as a device, an electronicapparatus (e.g., circuit, electronic system, or both), or a machine)including memory to store a list of sub-bands, a list of transmit points(TPs), and a list of user equipments (UEs) available to be scheduled ina wireless communication network; and processing circuitry coupled tothe memory and configured to estimate a three-dimensional matrix for thelist of UEs that includes a priority criterion value for each of the UEscalculated for each sub-band if served by each TP of the list of TPs;select an entry of the three-dimensional matrix that has a highest valuefor the priority criterion; and schedule a corresponding UE/TP pair tooperate on the corresponding sub-band represented in the entry.

In Example 21, the subject matter of Example 20 may optionally includewherein the processing circuitry is further configured to remove the UErepresented in the UE/TP pair from the list of available UEs subsequentto scheduling the corresponding UE/TP pair.

In Example 22, the subject matter of Example 21 may optionally includewherein the processing circuitry is further configured to remove the TPrepresented in the UE/TP pair from the list of available TPs subsequentto scheduling the corresponding UE/TP pair.

In Example 23, the subject matter of any of Examples 20-22 mayoptionally include wherein the priority criterion includes aproportional fairness criterion.

In Example 24, the subject matter of any of Examples 20-23 mayoptionally include wherein at least one TP can serve more than one UE innon-orthogonal multiple access (NOMA) mode.

In Example 25, the subject matter of any of Examples 20-24 mayoptionally include wherein the processing circuitry is configured toexecute the scheduling periodically based on a scheduling interval forthe wireless communication network.

In Example 26, the subject matter of any of Examples 20-25 mayoptionally include wherein the processing circuitry is furtherconfigured to prevent at least a subset of the list of TPs fromtransmitting on a subset of the list of sub-bands.

Example 27 includes subject matter (such as a device, an electronicapparatus (e.g., circuit, electronic system, or both), or a machine)including memory to store a list of user equipments (UEs) associatedwith the apparatus; and processing circuitry coupled to the memory andconfigured to make a scheduling decision based on a local observation ofa wireless network within which the apparatus is operating.

In Example 28, the subject matter of Example 27 may optionally includewherein the scheduling decision includes a decision as to whether topower down the apparatus for at least a scheduling interval.

In Example 29, the subject matter of Example 28 may optionally includewherein the scheduling decision is made based on reinforcement learning,and wherein the apparatus includes at least one aspect of a deepQ-network agent.

Example 30 includes subject matter (such as a device, an electronicapparatus (e.g., circuit, electronic system, or both), or a machine)including memory to store information received from a plurality of userequipments (UEs) having co-located hotspots; and processing circuitrycoupled to the memory and configured to determine configurationinformation for at least one co-located hotspot based on the informationreceived from a respective UE, the configuration information includingan identifier for a channel on which the at least one co-located hotspotis to operate, and a duration for which the configuration information isto remain valid; and provide configuration information to the at leastone co-located hotspot.

In Example 31, the subject matter of Example 30 may optionally includewherein the information includes interference information for one ormore channels on which the plurality of UEs are operating.

In Example 32, the subject matter of any of Examples 30-31 mayoptionally include wherein the information includes applicationinformation for one or more applications of the plurality of UEs.

In Example 33, the subject matter of any of Examples 30-32 mayoptionally include wherein the processing circuitry is configured todetermine configuration information using a machine learning (ML)algorithm.

In Example 34, the subject matter of Example 33 may optionally includewherein the ML algorithm predicts location for the at least one UE ofthe plurality of UEs based on historical information for at least the atleast one of the plurality of UEs.

What is claimed is:
 1. An apparatus comprising: memory to maintain alist of pairs of user equipments (UEs) and transmit points (TPs) withinan area; and processing circuitry coupled to the memory, the processingcircuitry configured to: designate an order for the list of pairs basedupon a priority criterion, a first pair of the list of pairs having ahighest priority based on the priority criterion; provide a firstmessage to a first TP corresponding to the first pair, the messageincluding an instruction to adjust transmission power to a firstoptimized power level based on an optimization function; and provide asecond message to a second TP corresponding to a second pair of the listof pairs, the second pair having a lower priority than the first pair,the message including an instruction to adjust transmission power to asecond optimized power level based on the optimization function andbased on the first optimized power level.
 2. The apparatus of claim 1,wherein if the optimized power level is less than or equal to about 10%of full transmission power for a TP within the area, the processingcircuitry is configured to instruct the respective TP to turn off. 3.The apparatus of claim 1, wherein the processing circuitry is configuredto provide messages to TPs sequentially according to priority of arespective pair of the list of pairs.
 4. The apparatus of claim 1,wherein the priority criterion includes a proportional fairnesscriterion.
 5. The apparatus of claim 1, wherein the optimizationfunction includes a weighted sum rate function.
 6. The apparatus ofclaim 5, wherein at least one pair of the list of pairs operates innon-orthogonal multiple access (NOMA), and wherein the order for thelist of pairs is based on a weighted NOMA sum rate.
 7. The apparatus ofclaim 5, wherein the optimization function is further based on actualchannel gain between a TP of a pair of the list of pairs and arespective UE of the pair.
 8. The apparatus of claim 1, furthercomprising transceiver circuitry and wherein the processing circuitry iscoupled to the transceiver circuitry and configured to provide the firstand second messages over a control channel using the transceivercircuitry.
 9. A method comprising: maintaining a list of pairs of userequipments (UEs) and transmit points (TPs) within an area; designatingan order for the list of pairs based upon a priority criterion, a firstpair of the list of pairs having a highest priority based on thepriority criterion; providing a message to a first TP corresponding tothe first pair, the message including an instruction to adjusttransmission power to a first optimized power level based on anoptimization function; and providing a message to a second TPcorresponding to a second pair of the list of pairs, the second pairhaving a lower priority than the first pair, the message including aninstruction to adjust transmission power to a second optimized powerlevel based on the optimization function and based on the firstoptimized power level.
 10. The method of claim 9, wherein if theoptimized power level is less than or equal to about 10% of fulltransmission power for a TP within the area, the method furthercomprises instructing the respective TP to turn off.
 11. The method ofclaim 9, further comprising providing messages to TPs sequentiallyaccording to priority of a respective pair of the list of pairs.
 12. Themethod of claim 9, wherein the optimization function includes a weightedsum rate function.
 13. The method of claim 12, wherein at least one pairof the list of pairs operates in nonorthogonal multiple access (NOMA),and wherein the order for the list of pairs is based on a weighted NOMAsum rate wherein the optimization function is further based on actualchannel gain between a TP of a pair of the list of pairs and arespective UE of the pair.
 14. The method of claim 12, furthercomprising providing the first and second messages over a controlchannel.
 15. The method of claim 9, wherein the priority criterionincludes a proportional fairness criterion.
 16. A non-transitorycomputer-readable medium including instructions that, when executed on aprocessor, cause the processor to perform operations comprising:maintaining a list of pairs of user equipments (UEs) and transmit points(TPs) within an area; designating an order for the list of pairs basedupon a priority criterion, a first pair of the list of pairs having ahighest priority based on the priority criterion; providing a message toa first TP corresponding to the first pair, the message including aninstruction to adjust transmission power to a first optimized powerlevel based on an optimization function; and providing a message to asecond TP corresponding to a second pair of the list of pairs, thesecond pair having a lower priority than the first pair, the messageincluding an instruction to adjust transmission power to a secondoptimized power level based on the optimization function and based onthe first optimized power level.
 17. The non-transitorycomputer-readable medium of claim 16, wherein if the optimized powerlevel is less than or equal to about 10% of full transmission power fora TP within the area, the instructions, when executed on the processor,further cause the processor to perform instructing the respective TP toturn off.
 18. The non-transitory computer-readable medium of claim 16,wherein the instructions, when executed on the process, further causethe processor to perform providing messages to TPs sequentiallyaccording to priority of a respective pair of the list of pairs.
 19. Thenon-transitory computer-readable medium of claim 16, wherein thepriority criterion includes a proportional fairness criterion.
 20. Thenon-transitory computer-readable medium of claim 16, wherein theinstructions, when executed on the process, further cause the processorto perform providing the first and second messages over a controlchannel.
 21. The non-transitory computer-readable medium of claim 16,wherein the optimization function includes a weighted sum rate function.22. The non-transitory computer-readable medium of claim 21, wherein atleast one pair of the list of pairs operates in non-orthogonal multipleaccess (NOMA).
 23. The non-transitory computer-readable medium of claim22, wherein the order for the list of pairs is based on a weighted NOMAsum rate.
 24. The non-transitory computer-readable medium of claim 16,wherein the optimization function is further based on actual channelgain between a TP of a pair of the list of pairs and a respective UE ofthe pair.